USE OF PRESCRIPTION MEDICATIONS WITH SOMNOLENCE AS A POTENTIAL ADVERSE EFFECT AMONG OLDER ADULTS IN THE UNITED STATES

Abstract Over half of community-dwelling older adults experience sleep disorders, with approximately 40% reporting somnolence or/and excessive daytime sleepiness, associated with an increased risk for cognitive impairment and premature mortality. The use and concurrent use of prescription medication with somnolence as an adverse effect may be an overlooked contributor to this growing problem. This study aims to characterize the use or concurrent use of medications with somnolence as an adverse effect and to assess associations between their use and the prevalence of somnolence (excessive daytime sleepiness or sleep duration of ≥9 hours) using data from the National Social Life, Health, and Aging Project (NSHAP) 2010-2011 (N=3,338). Adjusted prevalence was estimated from multivariable logistic regression models adjusted for socio-demographic measures and the number of medications without somnolence as an adverse effect. The estimated prevalence of somnolence was 54% for those reporting use of 3 or more medications with somnolence as an adverse effect vs. 36.5% for those not using such medications. The use of prescription medications with somnolence as a potential adverse effect was prevalent. Concurrent use of medications with somnolence as an adverse effect was associated with a higher prevalence of somnolence. These findings demonstrate the need for more research to understand the impact of concurrent use of medications with similar adverse side effects on OAs’ health and well-being.

the adjusted probability of self-neglect by social isolation, and interaction terms with gender.Results indicated the association between social isolation and self-neglect differed by gender (p-values for interaction: body neglect: 0.02, household neglect: 0.20).Among women, social isolation was associated with a higher risk of body neglect (social isolation: 26% vs no isolation: 14%, p=0.001) and household neglect (23% vs 17%, p=0.05).For men, social isolation was not associated with body neglect (27% vs 23% p=0.2) or household neglect (23% vs 22%, p=0.8).In summary, social isolation was associated with body and household neglect among women, but was not associated with neglect among men.Future work should investigate mechanisms for gender differences and interventions to address or prevent self-neglect through enhancing social connectedness.

LOCAL FAMILY AND FRIEND TIES AND THEIR RELATIONSHIP TO SOCIAL SUPPORT AND STRAIN AMONG OLDER ADULTS Won Choi, University of Chicago, Chicago, Illinois, United States
Family members and friends who live nearby are likely valuable sources of support for older adults.At the same time, local family and friend ties may also be a source of strain as spatial proximity to close ties can generate more intense interactions.Using data from Round 3 (2015-2016) of the National Social Life, Health, and Aging Project (NSHAP) (N=3,615), this study examines how local family and friend ties reported in older adults' social network roster are associated with instrumental and emotional support and social strain among community-dwelling older adults aged 50 and older.Results from ordered logistic regression models show that having a local friend tie is associated with higher levels of instrumental and emotional support from friends and lower levels of instrumental and emotional support from family.Having a local family tie, on the other hand, is associated with higher levels of instrumental support from family and lower levels of emotional support from friends.Having a local family tie is not related to emotional support from family or instrumental support from friends.Results also indicate that having a local friend tie increases the odds of reporting that friends make too many demands (i.e., higher friend strain) whereas having a local family tie is not a predictor of family strain.Together, results suggest that spatial proximity to friends and, to a lesser degree, family members are linked to how older adults experience social support and strain.

USE OF PRESCRIPTION MEDICATIONS WITH SOMNOLENCE AS A POTENTIAL ADVERSE EFFECT AMONG OLDER ADULTS IN THE UNITED STATES Jocelyn Wilder, NORC, Chicago, Illinois, United States
Over half of community-dwelling older adults experience sleep disorders, with approximately 40% reporting somnolence or/and excessive daytime sleepiness, associated with an increased risk for cognitive impairment and premature mortality.The use and concurrent use of prescription medication with somnolence as an adverse effect may be an overlooked contributor to this growing problem.This study aims to characterize the use or concurrent use of medications with somnolence as an adverse effect and to assess associations between their use and the prevalence of somnolence (excessive daytime sleepiness or sleep duration of ≥9 hours) using data from the National Social Life, Health, and Aging Project (NSHAP) 2010-2011 (N=3,338).Adjusted prevalence was estimated from multivariable logistic regression models adjusted for socio-demographic measures and the number of medications without somnolence as an adverse effect.The estimated prevalence of somnolence was 54% for those reporting use of 3 or more medications with somnolence as an adverse effect vs. 36.5% for those not using such medications.The use of prescription medications with somnolence as a potential adverse effect was prevalent.Concurrent use of medications with somnolence as an adverse effect was associated with a higher prevalence of somnolence.These findings demonstrate the need for more research to understand the impact of concurrent use of medications with similar adverse side effects on OAs' health and well-being.The current academic literature extensively used linear logistic models with social, behavioral, and psychological status to predict mortality.However, few address the interdependency of predictors and the imbalance of targets that adversely bias the results.Using the National Social Life Health and Aging Project (NSHAP), we developed two machine learning models predicting the 10-year mortality of older adults in the US.We first used tree-based algorithms of Decision Tree (DT) that account for the interdependency of the social features and decide the splitting nodes and thresholds using entropy gain conditional on the previous splitting predictor to discern disposition status.Second, we used the Fuzzy Support Vector Machine (FSVM) that regards every sample as a node in high-dimensional vector space and splits the nodes with an optimum plane by finding the best linear combination of features to get an optimum prediction accuracy.Additionally, FSVM addresses the target imbalance problem by conducting a more delicate classification of samples with close predicted probabilities of being alive and deceased.Compared to the accuracy rates achieved by the Logistic Regression, our algorithms perform better on the entire population and the population near the class boundary.We also discussed the social and demographic characteristics of the cases whose disposition statuses were either wrongly predicted as deceased or alive by our algorithms.The findings serve important purposes for public health practitioners in accurately understanding the risk and protective factors of mortality in aging.

EFFECTS OF DISCLOSING DIAGNOSES AND BIOMARKER RESULTS FOR ALZHEIMER'S DISEASE AND RELATED DISORDERS
Chair: Takashi Amano Discussant: Brian Carpenter Early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease and related dementias (ADRD) has been recognized as a key strategy to improve health-related outcomes.However, given that these conditions are highly stigmatized, receiving a diagnostic label or knowing about biomarkers related to MCI or ADRD may have a profound impact on the person's life.As the impacts of disclosure of diagnostic information related to MCI and ADRD on the well-being of people living with these conditions are not well understood, this symposium attempts to address this knowledge gap.The first presenter will describe a study that examined racial/ethnic variations in the effects of diagnostic labeling of ADRD on social aspects of the person's life.The second presenter will discuss findings from a systematic review of the effects of ADRD and cognitive impairment on social engagement.The third presenter will present the findings of the study on the effect of amyloid PET results disclosure on health-related behaviors of people with MCI.The fourth presenter will describe the results of an online vignette study on the outcomes of a preclinical Alzheimer disease diagnosis.The fifth presenter will discuss the use of blood biomarkers to aid in the diagnosis of ADRD in primary care (PC).At the end of the presentation, the discussant will highlight implications for future research and policy development to alleviate negative impacts and maximize the positive impacts of a diagnostic label of MCI and ADRD.
Abstract citation ID: igad104.1564Theoretically, knowing about one's diagnostic label of dementia may increase a chance to receive necessary social support.However, empirical evidence especially regarding the racial/ethnic variations in the effects of diagnostic labeling on provision of social support is lacking.Data from the Health and Retirement Study (HRS, 2000(HRS, -2018) ) were utilized to examine variations in the effects of knowing about a diagnostic labeling.A total of 7,192 person-year observations who had dementia were included in the analysis.Knowledge about a diagnostic label of dementia was measured by asking whether a doctor told the person that he/she had dementia.Formal and informal types of received social support were measured.Regression analyses with inverse probability weighting were performed.A moderating role of race/ethnicity was examined by conducting subgroup analyses.Knowing about a diagnostic label significantly increased likelihood of having more informal (OR = 1.58, p < 0.001, 95% CI = [1.34,1.86]) and formal support (b = 0.09,

PREDICTING 10 YEAR MORTALITY WITH MACHINE LEARNING: EVIDENCE FROM NATIONAL SOCIAL LIFE, HEALTH, AND AGING PROJECT Yiang
Li, University of Chicago, Chicago, Illinois, United States